A.I. and Finance: the new (Automated) White Collars?

Automated technologies have been implemented in multiple industries, from education to healthcare, and the banking system is riding the wave of technology opportunities too. Robotics helps identify and manage financial risks and evaluate credit limits, while Big Data Finance techniques extract the relevant elements of a dataset, reducing the amount of data to deal with. The FinTech world in general is providing structural and organizational changes in the way in which the financial system works.

A further step in automation is RegTech, that much like its cousin FinTech is promising to transform the finance system through tech innovations: short for Regulatory Technology, it is described as the adoption of new technologies to facilitate the delivery of regulatory requirements.

Banks in fact are trying to cope with the upcoming regulations that will impact the financial system, among which the Revised Payment Service Directive, the fourth AML Directive, and the revised Markets in Financial Instruments Directive, also known as MiFID II, coming into force in January 2018.

The difficulty in complying with such multitude of regulations, some of which of a significant technical complexity, has been perceived also by the regulators themselves. Testament to this is the recent European Commission decision to postpone the deadline for the MiFID II implementation by next year. “Given the complexity of the technical challenges highlighted by ESMA, it makes sense to extend the deadline for MiFID II” stated Jonathan Hill, Commissioner for Financial Services, Financial Stability and Capital Markets Union, that continued: “Meanwhile, we are pressing ahead with the level II legislation to implement MiFID II and expect to announce those measures shortly”.

RegTech solutions will play a vital role in managing the regulatory requirements, using machine learning systems that horizon scan for new regulations and map potential regulatory risks to key processes, thereby helping financial institutions to stay informed. Also, RegTech will use analytics mechanisms to automate customers and potential investors due diligences, simplifying the implementation of some regulatory aspects and driving down compliance costs, that are expected to reach over $230 billion only in 2018.

Such automation of processes will also help increase the effectiveness and accuracy of the results, cutting to the bone human errors and providing solutions on a (almost) real time basis. Also, RegTech solutions may be applied for a better understanding of how regulations can be used to improve the financial institutions’ performances, transforming legal burdens in new business strategies and opportunities.

The last feature, in particular, is at the core of the RegTech evolution: according to a recent report, the next generation of algorithms will be able to advise on financial issues, with RegTech providers already forecasting the benefits of a financial risks management without emotions, and some of the largest banks developing A.I. systems to come up with investment ideas.

Although the RegTech era is just getting started, more and more banks are investing on regulatory technologies, being already able to reap benefits from their use. Nonetheless, these automation forecast risks of malfunctioning algorithms in managing investment decisions and concerns surrounding the security of data and assets, as well as the shadow of job’s replacement by automated systems.

These issues have led experts to call for A.I. regulations, in particular regarding the possibility of being the subject of automated decision-making. As we know, the General Data Protection Regulation, coming into force in May 2018, already addresses the matter in Section 22, but much of the results will depend on how it will be interpreted by national courts and supervisory authorities. In a recent report, a research team at the Alan Turing Institute in London and the University of Oxford addressed such concerns, underlining the difference between what they termed as the “right to be informed” through which the data subjects may receive limited information about the logic involved and the consequences of automated or artificially intelligent decision making systems, and the wider “right to explanation”, viewed as an ideal mechanism to enhance accountability and transparency.

According to the experts, in order to effectively implement such right, companies’ obligations to reveal the purpose of an algorithm, the kinds of data it draws on to make its decisions, and other information needs to be followed by other legislative measures, such as a trusted third party with the power to scrutinize and audit algorithms, in order to ensure the transparency and fairness of the system.

Since the new era of financial automation is just getting started, we will have to wait (probably not for long) for new developments in the near future.

We will no doubt keep you posted.

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